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ATS Resume Optimizer

AI
audit your resume for ATS compatibility + get concrete fixes.
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Create an account and get 5 credits — no card, no daily limit.
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What you'll get: an ATS score with critical fixes, keyword gaps, format issues, and a suggested summary line. For another use, try AI · Resume Parser (export to CSV).

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Private & secure
AI tools process your PDF in memory, send only the extracted text to the model, and discard everything immediately. No file is persisted on our servers.
No watermarks
Clean output, no branding, no daily limits.
Pay only for what you use
Purchased credits never expire. Cancel anytime.

Why people use ATS Resume Optimizer

Most large companies route resumes through an ATS (Workday, Greenhouse, Lever, iCIMS, Naukri, Monster) before a human ever sees them. The optimizer audits ATS-friendliness: format compatibility (parseable layout), keyword density (matches the JD’s vocabulary), section ordering (recognized headers), and concrete fixes. Pairs with the JD Matcher for end-to-end job-application tuning.

Pre-application resume tune
Drop your current resume + (optionally) the JD, get format / keyword / structure audit with concrete fixes. Resolves the “why am I not getting interviews despite qualifications?” question by surfacing ATS-side failures invisible to humans.
Format conversion check
Convert your design-heavy Canva / InDesign resume to ATS-friendly format. The optimizer surfaces what to extract from sidebars, what to rewrite as plain text, what to reformat — all without losing content.
Career change rewriting
Switching industries means your existing resume keywords don’t match new-industry JDs. The optimizer (with target JD provided) surfaces vocabulary translation needed.
Senior-level resume audit
Senior roles use search-heavy ATSs that prioritize specific keywords (“P&L responsibility”, “org of N”, “stakeholder management”). The optimizer surfaces gaps even on resumes that look great to humans.
Recruiter pre-screen prep
Recruiters and career coaches use the optimizer to advise candidates on what to fix before the application. The structured fix list is more actionable than abstract feedback.

How ATS Resume Optimizer works

Three steps. Sign in to run, zero retention — files never persisted on our servers.

  1. STEP 1
    Drop the resume PDF
    PDF up to 25 MB. JD paste optional but recommended — with JD, audit becomes JD-targeted. Without, audit is generic-ATS readiness.
  2. STEP 2
    We audit format + content
    Format check: parseable layout (single-column wins; sidebars / multi-column lose), recognized section headers (EXPERIENCE / EDUCATION / SKILLS), font and graphic checks. Content check: keyword density (vs JD if provided), achievement-vs-responsibility framing, quantified impact.
  3. STEP 3
    Get a structured fix list
    Markdown output with: format issues / content issues / keyword gaps (if JD provided) / concrete fixes. Each fix has rationale (“ATSs often skip sidebars; move skills to main flow”) and severity (low / medium / high).

What makes pdfcraft ai different

  • Citations on every claim
    Every AI summary, answer, and translation links back to the page it came from. You can verify the source before relying on the output. Hallucination is bounded, not blind.
  • Pay only for what you use
    No subscription. Purchased credits never expire. Cancel anytime. Cost shown before you run — exact credit amount appears after upload.
  • Multi-provider routing, no vendor lock-in
    We balance multiple AI providers per call to optimise cost and quality. If one has an outage, the next call automatically tries another. You're not bound to a single vendor's pricing or downtime.
  • Zero retention by design
    Your PDF lives in memory only as long as the AI call needs it, then it's discarded — nothing is persisted to our servers. We don't fine-tune models on your data. Verifiable in your dashboard's audit log.
  • Honest about model limits
    When a doc exceeds the model's context window we surface that the output was truncated (we render result.wasTruncated in every AI tool's UI). When a number can't be verified from the source we flag it. No silent failures.

Frequently asked questions

Will the fixes work for all ATSs?
Generic ATS-friendliness covers ~90% of cases. Specific ATSs have minor quirks (Workday handles some formats Greenhouse doesn’t). The optimizer’s fixes are conservative — if your resume passes our audit, it almost certainly parses correctly across the major ATSs.
Should I keyword-stuff to match the JD?
No. Keyword stuffing is detectable (modern ATSs penalize density-without-context). Aim for natural keyword integration: if the JD says “stakeholder management”, describe a real instance of that. If you can’t, the role isn’t a fit.
What about Indian-specific ATS systems (Naukri, Monster, Foundit)?
Indian ATSs use slightly different vocabulary (CTC / notice period / primary skills / secondary skills). The optimizer recognizes these conventions and flags missing fields appropriately. Indian-specific resume formats (one-page vs detailed three-page tradition) noted in the format check.
Will it improve the quantified-achievement framing?
Surfaces sections where claims could be quantified (“led team” vs “led team of 8”, “improved performance” vs “improved performance by 30%”). Doesn’t fabricate numbers — you provide them. The optimizer just surfaces the opportunities.
What’s the credit cost?
3 credits per audit. Cost is fixed regardless of resume length.
Privacy?
Resume sent to inference provider for audit, not stored. For confidential job searches (especially when employed currently and not wanting current employer to see leaked drafts), redact name + current employer via Redact PDF first.

Need to score against a specific JD?

Resume ↔ JD Matcher gives you a fit percentage + per-requirement alignment table. Use it after the ATS audit to verify the optimized resume actually matches the target role.

Try Resume ↔ JD Matcher

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Use cases for this tool

Step-by-step workflows that put this tool to work.

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